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SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targets

arXiv 2605.16620 ↗First posted 15 May 2026cs.LGcited by 0

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Abstract (arXiv)

Learning causal relationships between variables from data is a fundamental research area with many applications across disciplines. Most existing causal discovery algorithms rely on the assumptions that (i) the underlying system is acyclic, (ii) the exogenous noise variables are Gaussian, and (iii) the intervention targets for the data-generating experiments are known. While these assumptions simplify the analysis, they are violated in real-life systems. Most existing methods that address these issues either assume the underlying model is linear or are constrained to operate in limited interventional settings. To that end, we propose SCOUT, a novel causal discovery framework for learning nonlinear cyclic causal relationships from soft interventional data with unknown targets. Our approach maximizes the data log-likelihood to recover the graph structure, using two normalizing-flow architectures: contractive residual flows and neural spline flows. Through experiments on synthetic and real-world data, we show that SCOUT outperforms state-of-the-art methods in both causal graph recovery and unknown target recovery across various interventional and noise settings.

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References · 25

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  11. 2017Permutation-based Causal Inference Algorithms with Interventionscited by 78
  12. 2019Permutation-Based Causal Structure Learning with Unknown Intervention Targetscited by 69
13 more
  1. 2019Scaling structural learning with NO-BEARS to infer causal transcriptome networkscited by 39
  2. 2015backShift: Learning causal cyclic graphs from unknown shift interventionscited by 37
  3. 2020Active Invariant Causal Prediction: Experiment Selection through Stabilitycited by 37
  4. 2013A Discovery Algorithm for Directed Cyclic Graphscited by 33
  5. 2020Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cyclescited by 31
  6. 2013Directed Cyclic Graphical Representations of Feedback Modelscited by 27
  7. 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
  8. 2013Cyclic Causal Discovery from Continuous Equilibrium Datacited by 21
  9. 2023NODAGS-Flow: Nonlinear Cyclic Causal Structure Learningcited by 20
  10. 2020Structure Learning for Cyclic Linear Causal Modelscited by 13
  11. 2022A Unified Experiment Design Approach for Cyclic and Acyclic Causal Modelscited by 7
  12. 2023Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Datacited by 7
  13. 2025Differentiable Cyclic Causal Discovery Under Unmeasured Confounderscited by 4

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